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Neural Network Training on Encrypted Data with TFHE
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We present an approach to outsourcing of training neural networks while preserving data confidentiality from malicious parties. We use fully homomorphic encryption to build a unified training approach that works on encrypted data and learns quantized neural network models. The data can be horizontally or vertically split between multiple parties, enabling collaboration on confidential data. We train logistic regression and multi-layer perceptrons on several datasets.
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Cited by 1 Pith paper
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ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping
ReBoot adapts CKKS homomorphic encryption, local-loss blocks, and a polynomial ReLU to train MLPs on encrypted data, but only one of its dataset results was produced by actually encrypted training.
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